T5 (small) fine-tuned on Text2Log

This model is a fine-tuned version of t5-small on an Text2Log dataset. It achieves the following results on the evaluation set:

• Loss: 0.0313

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 6

Training results

Usage:

from transformers import AutoTokenizer, T5ForConditionalGeneration

MODEL_CKPT = "mrm8488/t5-small-finetuned-text2log"

model = T5ForConditionalGeneration.from_pretrained(MODEL_CKPT).to(device)
tokenizer = AutoTokenizer.from_pretrained(MODEL_CKPT)

def translate(text):
    inputs = tokenizer(text, padding="longest", max_length=64, return_tensors="pt")
    input_ids = inputs.input_ids.to(device)
    attention_mask = inputs.attention_mask.to(device)

    output = model.generate(input_ids, attention_mask=attention_mask, early_stopping=False, max_length=64)

    return tokenizer.decode(output[0], skip_special_tokens=True)

prompt_nl_to_fol = "translate to fol: "
prompt_fol_to_nl = "translate to nl: "
example_1 = "Every killer leaves something."
example_2 = "all x1.(_woman(x1) -> exists x2.(_emotion(x2) & _experience(x1,x2)))"

print(translate(prompt_nl_to_fol + example_1)) # all x1.(_killer(x1) -> exists x2._leave(x1,x2))
print(translate(prompt_fol_to_nl + example_2)) # Every woman experiences emotions.

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.0+cu111
• Datasets 1.18.3
• Tokenizers 0.11.0